Today, data is the new business asset and businesses organizations need to be able to effectively manage their vast amount of data from various sources including online and offline. Having said that, it’s essential that organizations have an adequate infrastructure to store and process those data for various purposes such as Big Data analytics for hidden insights and/or advanced AI models training, ultimately applying AI into their existing business processes.
AI is Essential for Modern Businesses Successes
The rapid evolution of AI, Machine Learning, and Data Science makes it imperative for businesses to adopt AI to stay competitive. The advantages of adopting AI significantly outweigh the effort and time, as well as the costs of doing so.
For example, AI is used by many companies in the e-Commerce industry to personalize their customers’ experience by analyzing purchase history and browsing behavior to recommend relevant products and services. Additionally, AI-powered chatbots in customer services can help businesses handle FAQ (frequently asked questions), complementing human agent to provide 24/7/365 availability services, improving response times and freeing up human agents for more complex tasks.
Business organizations that do not have a clear strategy of AI adoption may fall behind. However, a considerable number of companies find it difficult to adopt AI across their businesses, and many AI projects fail to materialize. According to an article by Havard Business Review, the failure rate of AI projects has been high, with estimates of as high as 80%. The reasons many companies fail at or abandon their AI projects could be due to a poor data strategy, lack of risks management, as well as increasing development costs and other factors.
To overcome these AI solutions development and adoption challenges, businesses need a comprehensive strategy to plan well and prepare ahead.
Proof of Concept & Prototyping in AI Solutions Development
When it comes to AI solutions, one of the most common adoption strategies is to start small with a proofs of concept (PoC) or small-scale implementations first and gradually increasing to full-scale, enterprise-wide AI applications. To achieve a large-scale enterprise model, it is required of companies to have the infrastructure and systems with sufficient computational power for the processing of the massive volume of data, operating those complex AI models with a broad range of use cases.
A successful PoC could prove that an AI solution is feasible and beneficial to a business organization. However, a full-scale AI project will bring many much-needed business advantages such as performance enhancement and productivity improvements, informed data-driven decision making, etc. across businesses.
To leverage AI technology, businesses must collect and process a massive amount of data, ranging from data storage to data processing and data management, and data science, together with business process management.
To succeed with AI enterprise-wide adoption
Evolving an AI pilot project, from Proof of Concept to Full-Scale deployment is a complex process and companies typically need to follow one of these strategic processes as follow:
- AI use cases are selected and categorized according to potential business value, with the AI project teams focusing on prioritizing and scaling these important AI projects.
- AI uses cases are developed with Agile software methodology, end-users feedback and successful MVP is scaled.
How to Get Started with AI Solutions Development
AI Needs & Infrastructure Assessment
First, businesses need to embark on a company-wide AI landscape assessment including capabilities, use-cases, and infrastructure. Specifically, companies can evaluate their existing data strategy and data quality, as well as Ai models and overall AI’s strategy to understand the organization’s AI maturity.
Next, gaps in data strategy including data quality, and IT resources can be identified before kickstarting any AI development. As the saying goes garbage in, garbage out, the same applied to AI development. If an organization doesn’t have sufficient data of quality, it would very likely result in poor performing AI models. Moreover, without adequate computing resources, the capacity required to process large datasets or build complex models is also deterred and leads to project failures.
All in all, before embarking on projects, business organizations should start with a comprehensive AI needs evaluation about whether they truly need AI and if they have sufficient data and adequate infrastructure to proceed with AI development or not.
Use Cases Evaluation & Pilot Project Development
Once Ai use cases and needs as well as required resources are clearly established, business can start to prioritize use cases based on the business impact and value they bring. Those may involve factors such as revenue impacts, cost reductions, business process optimizations, and customer satisfaction.
Afterward, a pilot project to develop a prototype may be green-lit. For instance, we have assisted investment companies in building advanced data analytics and NLP solutions to optimize and evaluate their investment portfolios. These AI solutions enable firms to gather data from various public sources, such as Google Store reviews, social media, app stores, and directory websites.
Developing a prototype for this use case with detailed metrics and project results established a good starting point before building a full-scale solution for adopting AI enterprise-wide.
Final thoughts
Adopting AI across the entire business is a complex and multi-layers process that requires careful planning from data strategy and collection to processing and ML models training to deployment and adoption.
Many times, business organizations only see a fractional benefit of their early pilot AI projects and abandon their efforts altogether. The journey to adopting AI company-wide requires persevering through challenging phases. The real competitive advantages and significant AI capacities are usually realized after overcoming those challenges.
At Trustify Technology, we understand that building an AI solution from scratch is challenging and complicated and we can help businesses navigate the complexities of the process. Our team of AI engineers can provide you with expert guidance to develop your AI use cases and applications effectively. We cover all aspects of your AI solutions development and deployment, from data collection to model implementation and performance monitoring.